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Based on evolutionary game theory, this paper proposes a new information acquisition mechanism for intelligent mine construction, which solves the problem of incomplete information acquisition in the construction of new intelligent mining area and reduces the difficulty of information acquisition, which solves the problem of the imperfect mine information acquisition in the construction of a new smart mine regions and decreases the difficulty of a mine information acquisition. Based on the evolutionary game model, the perceptual incentive model based on group is established. The reliability of information collection is ensured by sharing and modifying the information collector. Through the analysis of the simulation results, it is found that the regional coverage model based on the cooperation in game theory and evolutionary game theory has a good effect on solving the bottleneck problem of the current intelligent mining area. This paper has an enlightening effect on the optimization of the mine information acquisition system. Through the improvement of the mine information acquisition system, the working efficiency of the information acquisition terminal can be effectively increased by 6%.

This paper studies the information of big data in the digital publishing industry chain and adopts advanced algorithms for its fusion calculation. The basic theory of digital publishing ecological chain is dissected, the construction requirements, construction methods, and construction paths of digital publishing ecological chain are analysed, and feasible construction measures are proposed. It also defines the connotation of the fusion of knowledge services between publishing institutions and libraries in the digital era; then analyses the characteristics and principles of the fusion of knowledge services between publishing institutions and libraries in the digital era; and finally sorts out the theoretical foundations such as synergy theory, information integration service theory, and game theory. Meanwhile, this paper also studies the flow of digital publishing resources and empirically analyses the eco-efficiency of digital publishing eco-chain by using metabolic network analysis, population analysis, and life cycle analysis, and it finds the eco-efficiency problems of the existing e-book eco-chain from its analysis. Finally, the imbalance in the digital publishing ecological chain and its hazards are analysed, and specific regulation and optimization measures are proposed. The research in this paper makes up for the deficiencies of related studies and can well solve the problems that exist in the previous and provide theoretical support for the healthy development of digital publishing enterprises.

In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different situations in order to pursue higher profits.

To minimize delay cost of flights in multiple airports, this paper studied flights assignment problem under delay conditions. By considering the delay cost, airport capacity, and the slot exchange between airlines, this paper proposed a novel assignment model based on game theory and CDM mechanism. An improved ant colony algorithm was proposed to solve the flight assignment problem. The case studies showed that the proposed model can optimize the minimum delay cost between airlines under multiairport capacity constraints. The performance of proposed method was better than that of traditional non-slot-exchange method.

This paper introduces a game theory approach to implement power flow distribution mission for a fuel cell/battery hybrid system considering uncertain power information. To fully describe the vying interaction relationship between the fuel cell and the battery, we design the power distribution problem as a noncooperative game problem, in which the fuel cell and the battery are deemed to be two interactional players, and each one chooses proper amount of power supply to maximize its own optimization function relying on the other chosen. Different from all previous research work in the published papers, the power demand information of the adopted driving cycle is assumed to be absolutely known. In this paper, we discuss the case that when the power demand is uncertain how the players act and the Nash Equilibrium can be effectively achieved. Three original contributions are made. First, we develop the utility function for each player taking into account the uncertain behavior of the power demand due to inaccurate prediction of driving cycle. Second, an iterative algorithm with a fuzzy logical controller for correction is proposed to reduce the influence of uncertain power demand information on the decisions of the players. Finally, the effectiveness is validated by a comparison simulation test.

Restructuring of electricity supply industry introduced the concept of deregulation. After deregulation transmission cost allocation is a vital issue. In the available literature, various authors have presented different methods for allocation of transmission cost. This paper presents the review of a variety of methods and algorithm based on the principle of power-flow tracing. For a fair and equitable transmission charge allocation it is necessary to know the quantum of power flowing by each generator and load in line flows. Power flow tracing techniques are mainly based on the concepts of proportional sharing principle, graph theory, circuit theory (Z-bus tracing), optimization approach, relative electrical distance concept, Equilateral bilateral exchange (EBE), and game theory. This paper presents a comprehensive review of all the available literature on the above.

This paper presents a formalized communicating process for dealing with information asymmetry between agents. A proactive process can improve the efficiency of dealing with asymmetry by allowing agents to take the initiative of communication in a goal-oriented way. In the process, by reasoning on belief and intention about the world and figuring out the information needed, the agent proactively requests information from another agent when asymmetry exists between them. Considering that agents may take advantage of information asymmetry by hiding information, the process also includes a model based on game theory to restrict the hiding behaviour. The work presented here not only introduces a definition of information asymmetry from cognitive perspective but also proposes a way to deal with it by communication in MAS. In addition, this paper presents some basic ideas on designing proactive mechanisms in cooperation between agents.

Aiming at the optimization problem of multiple objectives with contradictions and conflicts in the process of allocating the tolerance for complex products, taking advantage of the features of coordinating, and balancing contradictions and conflicts of cooperative game theory, this paper uses cooperative game theory to solve the multiobjective optimization problem of tolerance allocation. The quality requirements and cost requirements of assembly products are used as the game decision parties, and the fuzzy clustering method is used to group the design variables of tolerance allocation problem of the steam turbine to form the strategic space of game parties. Take the quality level and cost level of the assembly product as the optimization goals, complete the calculation of the utilities of the two game parties, and establish the multiobjective optimization model of tolerance allocation based on cooperative game theory. Finally, the Shapley value method based on cooperative game theory, the Nash equilibrium method based on noncooperative game theory, and the traditional single-objective optimization method with the quality as the constraint and the cost as the optimization objective are used to solve the tolerance allocation problem of steam turbine. The solution results show that the method of cooperative game realized the balance, coordination, and comprehensive optimization of the quality and cost from the perspective of collective interests, overcame the shortcomings of the traditional single-objective optimization method, and obtained better result than the Nash equilibrium method.

In recent years, countries all over the world consider the future internet as the country’s strategic development directions, so projects about future internet have been launched by these countries. Load balancing algorithms and job allocations are main research problems in areas of resource management of future internet. In this paper, we introduce a load balancing model for future internet. We formulate the static load balancing problem in the model proposed above as noncooperative game among users and cooperative game among processors. Based on this model, we derive a load balancing algorithm for computing center. Finally, we execute the algorithm presented in this paper with another three algorithms for comparison purpose. The advantages of our algorithm are better scalability to the model, improving system performance, and low cost on maintaining system information.

Gamification means the use of various elements of game design in nongame contexts including workplace collaboration, marketing, education, military, and medical services. Gamification is effective for both improving workplace productivity and motivating employees. However, introduction of gamification is not easy because the planning and implementation processes of gamification are very complicated and it needs interdisciplinary knowledge such as information systems, organization behavior, and human psychology. Providing a systematic decision making method for gamification process is the purpose of this paper. This paper suggests the decision criteria for selection of gamification platform to support a systematic decision making process for managements. The criteria are derived from previous works on gamification, introduction of information systems, and analytic hierarchy process. The weights of decision criteria are calculated through a survey by the professionals on game, information systems, and business administration. The analytic hierarchy process is used to derive the weights. The decision criteria and weights provided in this paper could support the managements to make a systematic decision for selection of gamification platform.

The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

Proactive defense is one of the most promising approaches to enhance cyber-security in the power systems, while how to balance its costs and benefits has not been fully studied. This paper proposes a novel method to model cyber adversarial behaviors as attackers contending for the defenders’ benefit based on the game theory. We firstly calculate the final benefit of the hackers and defenders in different states on the basis of the constructed models and then predict the possible attack behavior and evaluate the best defense strategy for the power systems. Based on a real power system subnet, we analyze 27 attack models with our method, and the result shows that the optimal strategy of the attacker is to launch a small-scale attack. Correspondingly, the optimal strategy of the defender is to conduct partial-defense.

To stimulate large-scale users to participate in the big data construction of IoT (internet of things), auction mechanisms based on game theory are used to select participants and calculate the corresponding reward in the process of crowdsensing data collection from IoT. In online auctions, bidders bid many times and increase their bid price. All the bidders want to maximize their utility in auctions. An effective incentive mechanism can maximize social welfare in online auctions. It is complicated for auction platforms to calculate social welfare and the utility of each bidder’s bidding items in online auctions. In this paper, a transaction trade-off utility incentive mechanism is introduced. Based on the transaction trade-off utility incentive mechanism, it can make the forecasting process consistent with bidding behaviors. Furthermore, an end-price dynamic forecasting agent is proposed for predicting end prices of online auctions. The agent develops a novel trade-off methodology for classifying online auctions by using the transaction trade-off utility function to measure the distance of auction items in KNN. Then, it predicts the end prices of online auctions by regression. The experimental results demonstrate that an online auction process considering the transaction utility is more consistent with the behaviors of bidders, and the proposed prediction algorithm can obtain higher prediction accuracy.

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.

This paper investigates a pricing game and service cooperation for complementary products in a dual-channel supply chain composed of two manufacturers and one retailer. The products of the two manufacturers are complementary products. One manufacturer sells products simultaneously through its own online channel and the traditional retailer, and the manufacturer delivers the product’s service to the retailer in its network direct sales channel by cooperating with the retailer in the form of service cost sharing. Considering the different market power structures of channel members, we establish three different pricing game models. By using the backward induction method and game theory, we obtain the corresponding analytical equilibrium solutions. Then, the service cooperation strategy of using the channel service sensitivity coefficients to construct the weight to share the service cost is proposed. Finally, numerical examples of optimal pricing strategies and profit conditions in different game situations are given, and sensitivity analysis of some key parameters is selectively performed, in which some valuable management insights are obtained.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

In high density urban areas, pedestrians have a great influence on the capacity of intersections. This paper studies the influence of pedestrians on road capacity and proposes an exclusive right-turn lane capacity model considering pedestrian-vehicle interaction (PV-RTC). Firstly, a pedestrian-vehicle interaction (PVI) model is proposed based on the logit model and static games theory of incomplete information. Through this model, the probability of 6 kinds of pedestrian-vehicle interaction situations (vehicles yield to pedestrians, pedestrians yield to vehicles, etc.) in the crosswalk can be obtained. Then, based on the basic idea of the stop line method and the probabilities of above situations, the PV-RTC model is established, and the sensitivity analysis of the important factors (pedestrian arrival rate, yielding rate, and green time ratio) affecting the model is carried out to clarify the mechanism of the proposed model. Finally, a pedestrian-vehicle interaction model of cellular automata for the exclusive right-turn lane is established and its simulation results are compared with the results of the PV-RTC model. The results show that the relative error between the microscopic simulation model and PV-RTC model is less than 15% overall, which verifies the validity of the PV-RTC model. This study provides references for a more precise estimation method of pedestrian impact on road capacity.

Because of the complexity of the environment and the limited resources of nodes, there will be an imbalanced energy consumption and a short life among nodes in the wireless sensor network. In this paper, by introducing the concept of game theory and supermodel game theory, we solve the challenge of a wireless sensor network topology control method based on cross-layer information design. The cross-layer information such as node degree, network connectivity, and MAC layer interference is integrated into the design of utility function to establish a new topological game model. Then, based on this topology control model, we propose a cross-layer optimized energy-balanced topology control algorithm (COETC). Compared with other algorithms, our COETC algorithm not only guarantees the network connectivity and robustness while reducing the required node transmitting power but also achieves good energy balance and high energy efficiency. Ultimately, our method effectively prolongs network lifetime and improves network performance.

In the application of uncertainty measure theory, the determination method of index weight mainly includes the subjective weight determination method and the objective weight determination method. The subjective weight determination method has the disadvantages affected by the subjective preference of the decision-maker. The objective weight determination method often ignores the participation degree of the decision-maker, and when using the uncertainty measure evaluation model to perform multi-index classification evaluation, the credible degree recognition criterion is often used as the attribute recognition of the object to be measured, because the credible degree is taken by the subjective people, and the different values of different people have a great influence on the evaluation results. In order to solve the above problems in the uncertainty measure theory, this paper used the combination weighting of game theory to determine the optimal weight. At the same time, the credible degree recognition criterion was improved on the basis of the concept of minimum uncertainty measure distance, and a game theory-improved uncertainty measure optimization model was proposed. Finally, the validity of the model was proven by a case.

National healthcare systems in all countries do not act effectively. Therefore, especially strategies for introducing organizational innovation to public organization should be considered. The problem is how to organize the research in this field. One of the generally accepted solutions is the systemic approach to healthcare systems. In this paper multiagent systems theory and autonomous systems theory are applied to the analysis of main types of healthcare systems. Such analysis allows us to consider the system properties: the level of the autonomy, energy dissipation in the system, the payoff specificity (in the meaning of game theory), functional role of the agents in the system, the level of the agents’ cooperation, and delays in flows of money, requests, rules, and controls. As a result, some new functionalities of the healthcare system on the national level have been found and analysed. The aforementioned parameters are good tools to analyse the system functionality.

Viewing from the perspective of a traditional manufacturing enterprise, the paper proposes a fuzzy comprehensive evaluation system model, which selects the most suitable supplier or suppliers to undertake the supply tasks and to achieve the purposes of reducing procurement costs and improving the supply quality and reliability, so as to fully meet customer demands. Based on decision-makers’ preferences and suppliers’ supply capabilities, subjective and objective weight of criteria are both developed for supplier selection with the corresponding weighting method, namely, quality function deployment (QFD) and entropy weight. Specially, the two-phase fuzzy QFD method combined with the trapezoidal fuzzy number (TFN) is creatively applied to realize the complicated conversion process. Furthermore, the game theory is employed to combine the advantages of the subjective and objective weighting method, by which the comprehensive weight can be determined. A numerical example is given to demonstrate the proposed modeling process. Through programming drawing images for comparative analysis and sensitivity analysis, study image trends, and draw conclusions, the proposed model effectively alleviates the weight deviation and has higher rationality and extensibility in supplier selection.

The Internet of Medical Things (IoMT) is an amalgamation of smart devices to operate the wireless body area network (WBAN) by using networking technologies. To reduce the burden on WBANs, they link to the mobile edge computing (MEC), on which captured medical data can be stored and analyzed. In this paper, we design a new control scheme to effectively share the limited computation and communication resources in the MEC-assisted WBAN (M-W) platform. Based on the bargaining game theory, our proposed scheme explores the mutual benefits of intra- and inter-WBAN interactions. To dynamically adapt the current system conditions, we shape each WBAN’s aspirations to reach a reciprocal consensus for different application services. Utilizing two control factors, we provide a unifying framework for the study of intra- and inter-WBAN bargaining problems to share the limited system resource. Based on the feasibility and real-time effectiveness, the main novelty of the proposed scheme is the ability to achieve a relevant tradeoff between efficiency and fairness through the interactive bargaining process. At last, the experimental results show that the proposed scheme achieves substantial performance improvements to the comparison schemes.

For the problem of taxi carpooling detour, this paper studies driver strategy choice with carpooling detour. The model of taxi driver strategy evolution with carpooling detour is built based on prospect theory and evolution game theory. Driver stable strategies are analyzed under the conditions of complaint mechanism and absence of mechanism, respectively. The results show that passenger’s complaint mechanism can effectively decrease the phenomenon of driver refusing passengers with carpooling detour. When probability of passenger complaint reaches a certain level, the stable strategy of driver is to take carpooling detour passengers. Meanwhile, limiting detour distance and easing traffic congestion can decrease the possibility of refusing passengers. These conclusions have a certain guiding significance to formulating taxi policy.

Green communications are playing critical roles in vehicular ad hoc networks (VANETs), while the deployment of a power efficient VANET is quite challenging in practice. To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution. This paper has employed Nash Equilibrium in the noncooperative game model and analyzes its efficiency. Simulation results have shown an obvious improvement on power efficiency through such efforts.

Rapidly and correctly assessing the extent of the damage to a concrete structure after a disastrous fire has great engineering significance. In this paper, an assessment method is proposed based on the game theory-normal cloud model. The surface colors of concrete, exfoliation, cracks, and hammering responses are chosen as the comprehensive assessment indices of concrete damage after fire to establish the corresponding assessment criteria. The normal cloud generator is used to calculate the certainty degree of the grading assessment index of concrete damage after fire. By adopting game theory, the objective weight obtained by the information entropy method and the subjective weight obtained by precedence chart are combined into a comprehensive weight. The comprehensive certainty degree value of concrete damage after fire is then calculated by certainty and weight matrices. The project case analysis shows the assessment results are both intuitive and realistic. This method provides a new approach to the assessment of concrete damage after fire which is more efficient and easier to apply than existing methods.

Summary The installation of energy storage system to smooth the fluctuations of wind power output connected to the grid can effectively improve the electric quality and increase economic benefits of wind-generated electricity. Aiming at the overall profit maximization of wind power generation and storage system (WPGSS), taking the smoothing effect of active power output, the cost of hybrid energy storage system, and the earnings of wind power connected to the grid into consideration, this paper brings forward a game theory-based coordination and optimization control methodology for WPGSS adopting two low-pass filters to smooth the fluctuation of wind power output. The smoothing control to the active power of wind power and the power distribution of the hybrid energy storage system is respectively achieved by regulating the time constants of the two filters. With the combinations of wind power–batteries and wind power–super-capacitors as game's participants and the maximization of the WPGSS's overall profit as game's goal constraint conditions, game theory is introduced to realize the coordination and optimization between the time constants of the two filters. For comparative analysis, the model's optimal solution is respectively worked out by genetic algorithm and particle swarm optimization. The simulation results verify the effectiveness of the proposed method and provide a theoretical basis for the economic evaluation of the WPGSS. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Distributed generation (DG) units are increasingly installed in the power systems. Distribution companies (DisCo) can opt to purchase the electricity from DG in an energy purchase contract to supply the customer demand and reduce energy loss. This paper proposes a framework for optimal contract pricing of independent dispatchable DG units considering competition among them. While DG units tend to increase their profit from the energy purchase contract, DisCo minimizes the demand supply cost. Multi-leader follower game theory concept is used to analyze the situation in which competing DG units offer the energy price to DisCo and DisCo determines the DG generation. A bi-level approach is used to formulate the competition in which each DG problem is the upper-level problem and the DisCo problem is considered as the lower-level one. Combining the optimality conditions of all upper-level problems with the lower-level problem results in a multi-DG equilibrium problem formulated as an equilibrium problem with equilibrium constraints. Using a nonlinear approach, the equilibrium problem with equilibrium constraints problem is reformulated as a single nonlinear optimization model, which is simultaneously solved for all independent DG units. The proposed framework was applied to the modified IEEE 34-bus distribution test system. Performance and robustness of the proposed framework in determining econo-technically fair DG contract price has been demonstrated through a series of analyses. Copyright © 2016 John Wiley & Sons, Ltd.

Game Theory is a promising approach to acquire coalition formations in multiagent systems. This paper is focused on the importance of the distributed computation and the dynamic formation and reformation of pursuit groups in pursuit-evasion problems. In order to address this task, we propose a decentralized coalition formation algorithm based on the Iterated Elimination of Dominated Strategies (IEDS). This Game Theory process is common to solve problems requiring the withdrawal of dominated strategies iteratively. Furthermore, we have used the Markov Decision Process (MDP) principles to control the motion strategy of the agents in the environment. The simulation results demonstrate the feasibility and the validity of the given approach in comparison with different decentralized methods.

To properly protect ancient buildings, it is vitally important to determine the health status of these structures in a timely manner. To easily, quickly, reliably, and quantitatively assess the structure health of ancient timber buildings, this paper proposes a structure health assessment method based on fuzzy matter-element theory improved by asymmetric proximity, and the specific assessment steps are given. First, a fuzzy matter-element model is constructed; then, to effectively solve the failure problem of the maximum membership principle, the fuzzy matter-element assessment model is improved by using the theory of asymmetric proximity. The weights determined by the entropy weight and clustering methods are then combined through the game theory combination weighting concept and, finally, the structural condition of the ancient timber buildings is classified into five grades: I favorable, II ordinary, III poor, IV inferior, and V dangerous. Ten evaluation factors were chosen: bearing capacity of the foundation soil, subbase deformation, foundation damage, column and foundation connection, bearing capacity of members, connection construction, member deformation, crack slope, crack depth, and decayed/insect attacked. Taking a building built in the Republican Period in Sino-Ocean Taikoo Li, Chengdu, as an example, this model was used to assess the building’s structural condition. The results show that this method is feasible to quantitatively assess the structural condition of ancient timber buildings. It is a simple and practical assessment method that can provide a decision-making basis for maintenance, protection, and reinforcement of ancient timber buildings.

Psychological experiment studies reveal that human interaction behaviors are often not the same as what game theory predicts. One of important reasons is that they did not put relevant constraints into consideration when the players choose their best strategies. However, in real life, games are often played in certain contexts where players are constrained by their capabilities, law, culture, custom, and so on. For example, if someone wants to drive a car, he/she has to have a driving license. Therefore, when a human player of a game chooses a strategy, he/she should consider not only the material payoff or monetary reward from taking his/her best strategy and others' best responses but also how feasible to take the strategy in that context where the game is played. To solve such a game, this paper establishes a model of fuzzily constrained games and introduces a solution concept of constrained equilibrium for the games of this kind. Our model is consistent with psychological experiment results of ultimatum games. We also discuss what will happen if Prisoner's Dilemma and Stag Hunt are played under fuzzy constraints. In general, after putting constraints into account, our model can reflect well the human behaviors of fairness, altruism, self-interest, and so on, and thus can predict the outcomes of some games more accurate than conventional game theory.

The paper describes an evolution game model of travel mode choice to determine whether transportation policies would have the desired effect. The model is first expressed as a two-stage sequential game in the extensive form based on the similarity between evolution game theory and the travel mode choice process. Second, backward induction is used to solve for Nash equilibrium of the game based on the Folk Theorem. Third, the sensitivity analysis suggests that a payoff reduction of travel by any mode will result in a rising proportion of inhabitants travelling by that mode and falling proportions of inhabitants travelling by other modes. Finally, the model is applied to Beijing inhabitants’ travel mode choices during morning peak hours and draws the conclusion that the proportion of inhabitants travelling by rail would increase when traffic congestion is more severe. This confirms that fast construction of the urban rail transit would be an effective means of alleviating traffic congestion. The model may be a useful tool for policy makers for analyzing the complex influence of travel mode choice processes on transport policies and transport construction projects.

The Internet has become an evolving entity, growing in importance and creating new value through its expansion and added utilization. The Internet of Things (IoT) is a new concept associated with the future Internet and has recently become popular in a dynamic and global network infrastructure. However, in an IoT implementation, it is difficult to satisfy different Quality of Service (QoS) requirements and achieve rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. Based on the Markov game model, the proposed scheme can effectively allocate IoT resources while maximizing system performance. In multiagent environments, a game theory approach can provide an effective decision-making framework for resource allocation problems. To verify the results of our study, we perform a simulation and confirm that the proposed scheme can achieve considerably improved system performance compared to existing schemes.

The goal of contact tracing is to reduce the likelihood of transmission, particularly to individuals who are at greatest risk for developing complications of infection, as well as identifying individuals who are in need of medical treatment of other interventions. In this paper, we develop a simple mathematical model of contact investigations among a small group of individuals and apply game theory to explore conflicts of interest that may arise in the context of perceived costs of disclosure. Using analytic Kolmogorov equations, we determine whether or not it is possible for individual incentives to drive noncooperation, even though cooperation would yield a better group outcome. We found that if all individuals have a cost of disclosure, then the optimal individual decision is to simply not disclose each other. With further analysis of (1) completely offsetting the costs of disclosure and (2) partially offsetting the costs of disclosure, we found that all individuals disclose all contacts, resulting in a smaller basic reproductive number and an alignment of individual and group optimality. More data are needed to understand decision making during outbreak investigations and what the real and perceived costs are. © 2014 Nicolas Sippl-Swezey et al.

On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms.

By applying the game theory, the relationship between airline ticket price and optimal flight frequency is analyzed. The paper establishes the payoff matrix of the flight frequency in noncooperation scenario and flight frequency optimization model in cooperation scenario. The airline alliance profit distribution is converted into profit distribution game based on the cooperation game theory. The profit distribution game is proved to be convex, and there exists an optimal distribution strategy. The results show that joining the airline alliance can increase airline whole profit, the change of negotiated prices and cost is beneficial to profit distribution of large airlines, and the distribution result is in accordance with aviation development.

The paper has as a starting point the work of the philosopher Professor D. Lewis. We provide a detailed presentation and complete analysis of the sender/receiver Lewis signaling game using a game theory extensive form, decision tree formulation. It is shown that there are a number of Bayesian equilibria. We explain which equilibrium is the most likely to prevail. Our explanation provides an essential step for understanding the formation of a language convention. The informational content of signals is discussed and it is shown that a correct action is not always the result of a truthful signal. We allow for this to be reflected in the payoff of the sender. Further, concepts and approaches from neighbouring disciplines, notably economics, suggest themselves immediately for interpreting the results of our analysis (rational expectations, self-fulfilling prophesies).

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.

To achieve a valid effect of wireless mesh networks against selfish nodes and selfish behaviors in the packets forwarding, an approach named mixed MPS-BNS strategy is proposed in this paper. The proposed strategy is based on the Maximum Payoff Strategy (MPS) and the Best Neighbor Strategy (BNS). In this strategy, every node plays a packet forwarding game with its neighbors and records the total payoff of the game. After one round of play, each player chooses the MPS or BNS strategy for certain probabilities and updates the strategy accordingly. In MPS strategy, each node chooses a strategy that will get the maximum payoff according to its neighbor’s strategy. In BNS strategy, each node follows the strategy of its neighbor with the maximum total payoff and then enters the next round of play. The simulation analysis has shown that MPS-BNS strategy is able to evolve to the maximum expected level of average payoff with faster speed than the pure BNS strategy, especially in the packets forwarding beginning with a low cooperation level. It is concluded that MPS-BNS strategy is effective in fighting against selfishness in different levels and can achieve a preferable performance.

Emergent content-oriented networks prompt Internet service providers (ISPs) to evolve and take major responsibility for content delivery. Numerous content items and varying content popularities motivate interdependence between peering ISPs to elaborate their content caching and sharing strategies. In this paper, we propose the concept of peering for content exchange between interdependent ISPs in content centric Internet to minimize content delivery cost by a proper peering strategy. We model four peering strategic games to formulate four types of peering relationships between ISPs who are characterized by varying degrees of cooperative willingness from egoism to altruism and interconnected as profit-individuals or profit-coalition. Simulation results show the price of anarchy (PoA) and communication cost in the four games to validate that ISPs should decide their peering strategies by balancing intradomain content demand and interdomain peering relations for an optimal cost of content delivery.

Game theory may offer a useful mechanism to address many problems in mobile ad hoc networks (MANETs). One of the key concepts in the research field of such networks with Optimized Link State Routing Protocol (OLSR) is the security problem. Relying on applying game theory to study this problem, we consider two strategies during this suggested model: cooperate and not-cooperate. However, in such networks, it is not easy to identify different actions of players. In this paper, we have essentially been inspired from recent advances provided in game theory to propose a new model for security in MANETs. Our proposal presents a powerful tool with a large number of players where interactions are played multiple times. Moreover, each node keeps a cooperation rate (CR) record of other nodes to cope with the behaviors and mitigate aggregate effect of other malicious devices. Additionally, our suggested security mechanism does not only take into consideration security requirements, but also take into account system resources and network performances. The simulation results using Network Simulator 3 are presented to illustrate the effectiveness of the proposal.

Small power plants and buildings with renewable power generation capability have recently been added to traditional central power plants. Through these facilities, prosumers appear to have a concurrent role in both energy production and consumption. Based on bidirectional power transfers by large numbers of prosumers, a smart microgrid has become an important factor in efficiently controlling the microgrids used in power markets and in conducting effective power trades among grids. In this paper, we present an approach utilizing the game theory for effective and efficient energy routing, which is a novel and challenging procedure for a smart microgrid network. First, we propose strategies for choosing the desired transaction price for both electricity surpluses and shortages to maximize profits through energy transactions. An optimization scheme is utilized to search for an energy route with minimum cost using the solving method used in a traditional transportation problem by treating the sale and purchase quantities as transportation supply and demand, respectively. To evaluate the effect of the proposed decision strategies, we simulated our mechanism, and the results proved that our mechanism yields results pursued by each strategy. Our proposed strategies will contribute to spreading a smart microgrid for enhancing the utilization of microgrids.

In practical decision making problems, whether an alternative (strategy) of a decision maker is good or not relies on not only influences of its natural characters and external environment but also interactions with the decision maker's rivals. Thus for the sake of more accurate and actual assessments, in this paper we analyze alternatives (strategies) quantitatively based on both multi-attribute decision making methods and game theory. A multi-attribute aggregation based game model is then constructed. For general games, we first divide strategies into four forms after investigating the traits of practical game problems, and analyze relationships of these forms of strategies. Then four classifications of general game problems are presented according to the differences of strategies involved. Based on the game model, general methods to solve these four classifications of games are put forward, and relevant examples are taken to illustrate the flexibility of the methods. Moreover, we investigate the methods for solving multicriteria games, and finally extend them to general forms on the basis of the game model. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

With more complex structures and denser populations, congestion is a crucial factor in estimating evacuation clearance time. This paper presents a novel evacuation model that implements a game theory combining the greatest entropy optimization criterion with stochastic Monte Carlo methods to optimize the congestion problem and other features of emergency evacuation planning. We introduce the greatest entropy criterion for convergence to Nash equilibrium in then-person noncooperative game. The process of managing the conflict problem is divided into two steps. In the first step, we utilize Monte Carlo methods to evaluate the risk degree of each route. In the second step, we propose an improved method based on game theory, which obtains an optimal solution to guide the evacuation of all agents from the building.

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

Determining the winners in combinatorial auctions to maximize the auctioneer's revenue is an NP-complete problem. Computing an optimal solution requires huge computation time in some instances. In this paper, we apply three concepts of the game theory to design an approximation algorithm: the stability of the Nash equilibrium, the self-learning of the evolutionary game, and the mistake making of the trembling hand assumption. According to our simulation results, the proposed algorithm produces near-optimal solutions in terms of the auctioneer's revenue. Moreover, reasonable computation time is another advantage of applying the proposed algorithm to the real-world services.

The aim of this paper is to develop an effective method for solving bimatrix games with payoffs of intuitionistic fuzzy value. Firstly, bimatrix game model with intuitionistic fuzzy payoffs (IFPBiG) was put forward. Secondly, two kinds of nonlinear programming algorithms were discussed with the Nash equilibrium of IFPBiG. Thirdly, Nash equilibrium of the algorithm was proved by the fixed point theory and the algorithm was simplified by linear programming methods. Finally, an example was solved through Matlab; it showed the validity, applicability, and superiority.